OpenRaiser/Intern-Atlas huggingface dataset:
ProfilingAgent: Profiling-Guided Agentic Reasoning for Adaptive Model Optimization
Venue: arXiv cs.LG 2025
Year: 2025

Foundation models face growing compute and memory bottlenecks, hindering deployment on resource-limited platforms. While compression techniques such as pruning and quantization are widely used, most rely on uniform heuristics that ignore architectural and runtime heterogeneity. Profiling tools expose per-layer latency, memory, and compute cost, yet are rarely integrated into automated pipelines. We propose ProfilingAgent, a profiling-guided, agentic approach that uses large language models (LLMs) to automate compression via structured pruning and post-training dynamic quantization. Our modular multi-agent system reasons over static metrics (MACs, parameter counts) and dynamic signals (latency, memory) to design architecture-specific strategies. Unlike heuristic baselines, ProfilingAgent tailors layer-wise decisions to bottlenecks. Experiments on ImageNet-1K, CIFAR-10, and CIFAR-100 with ResNet-101, ViT-B/16, Swin-B, and DeiT-B/16 show pruning maintains competitive or improved accuracy (about 1% drop on ImageNet-1K, +2% gains for ViT-B/16 on smaller datasets), while quantization achieves up to 74% memory savings with <0.5% accuracy loss. Our quantization also yields consistent inference speedups of up to 1.74 times faster. Comparative studies with GPT-4o and GPT-4-Turbo highlight the importance of LLM reasoning quality for iterative pruning. These results establish agentic systems as scalable solutions for profiling-guided model optimization.
Fixed Parameter Tractable Linearizability Monitoring for Stack, Queue and Anagram Agnostic Data Types
Venue: arXiv cs.PL 2025
Year: 2025

Verifying linearizability of concurrent data structures is NP-hard, even for simple types. We present fixed-parameter tractable algorithms for monitoring stacks, queues, and anagram-agnostic data types (AADTs), parameterized by the maximum concurrency. Our approach leverages frontier graphs and partition states to bound the search space. For AADTs, equivalence of linearizations enables monitoring in log-linear time. For stacks, we introduce a grammar-based method with a sub-cubic reduction to matrix multiplication, and for queues, a split-sequence transition system supporting efficient dynamic programming. These results unify tractability guarantees for both order-sensitive and anagram-agnostic data types under bounded concurrency.
Language Model Perplexity Predicts Scientific Surprise and Transformative Impact
Venue: arXiv cs.SI 2025
Year: 2025

Scientific breakthroughs typically emerge through the surprising violation of established research ideas, yet quantifying surprise has remained elusive because it requires a coherent model of all contemporary scientific worldviews. Deep neural networks like large language models (LLMs) are arbitrary function approximators tuned to consistently expect the expressions and ideas on which they were trained and those semantically nearby. This suggests that as LLMs improve at generating plausible text, so the perplexity or improbability a text sequence would be generated by them should come to better predict scientific surprise and disruptive importance. Analyzing over 2 million papers across multiple disciplines published immediately following the training of 5 prominent open LLMs, here we show that higher perplexity scores systematically predict papers that receive more variable review ratings, longer editorial delays, and greater reviewer uncertainty. The most perplexing papers exhibit bimodal outcomes: disproportionately represented among the most celebrated scientific achievements and also the most discounted. High-perplexity papers tend to be published in journals with more variable impact factors and receive fewer short-term citations but in prestigious venues that bet on long-term impact. They also generate more interdisciplinary engagement portending long-term influence, and are more likely to have been supported by speculative funders like DARPA versus the NIH. Interestingly, we find the opposite pattern for humanities research, where the least surprising work is the most celebrated and cited. Our findings reveal that computational measures of corpus-wide linguistic surprise can forecast the reception and ultimate influence of scientific ideas, offering a scalable approach to recognize and generate potentially transformative research that challenge conventional scientific thinking.
CC-GSEO-Bench: A Content-Centric Benchmark for Measuring Source Influence in Generative Search Engines
Venue: arXiv cs.CL 2025
Year: 2025

Generative Search Engines (GSEs) synthesize conversational answers from multiple sources, weakening the long-standing link between search ranking and digital visibility. This shift raises a central question for content creators: How can we reliably quantify a source article's influence on a GSE's synthesized answer across diverse intents and follow-up questions? We introduce CC-GSEO-Bench, a content-centric benchmark that couples a large-scale dataset with a creator-centered evaluation framework. The dataset contains over 1,000 source articles and over 5,000 query-article pairs, organized in a one-to-many structure for article-level evaluation. We ground construction in realistic retrieval by combining seed queries from public QA datasets with limited synthesized augmentation and retaining only queries whose paired source reappears in a follow-up retrieval step. On top of this dataset, we operationalize influence along three core dimensions: Exposure, Faithful Credit, and Causal Impact, and two content-quality dimensions: Readability and Structure, and Trustworthiness and Safety. We aggregate query-level signals over each article's query cluster to summarize influence strength, coverage, and stability, and empirically characterize influence dynamics across representative content patterns.
Accuracy of Mathematical Functions in Julia
Venue: arXiv cs.MS 2025
Year: 2025

Basic computer arithmetic operations, such as $+$, $\times$, or $÷$ are correctly rounded, whilst mathematical functions such as $e^x$, $\ln(x)$, or $\sin(x)$ in general are not, meaning that separate implementations may provide different results when presented with an exact same input, and that their accuracy may differ. We present a methodology and a software tool that is suited for exhaustive and non-exhaustive testing of mathematical functions of Julia in various floating-point formats. The software tool is useful to the users of Julia, to quantise the level of accuracy of the mathematical functions and interpret possible effects of errors on their scientific computation codes that depend on these functions. It is also useful to the developers and maintainers of the functions in Julia Base, to test the modifications to existing functions and to test the accuracy of new functions. The software (a test bench) is designed to be easy to set up for running the accuracy tests in automatic regression testing. Our focus is to provide software that is user friendly and allows to avoid the need for specialised knowledge of floating-point arithmetic or the workings of mathematical functions; users only need to supply a list of formats, choose the rounding modes, and specify the input space search strategies based on how long they can afford the testing to run. We have utilized the test bench to determine the errors of a subset of mathematical functions in the latest version of Julia, for binary16, binary32, and binary64 IEEE 754 floating-point formats, and found $0.49$ to $0.51$ULPs in binary16, and $0.5$ to $2.4$ULPs of error in binary32 and binary64. The functions that may be correctly rounded (error of $0.5$ULP) in all the three formats are sqrt and cbrt. The following functions may be correctly rounded only for binary16: sinh, asin, cospi, sinpi, atanh, log2, tanh.
Revealing the Numeracy Gap: An Empirical Investigation of Text Embedding Models
Venue: arXiv cs.CL 2025
Year: 2025

Text embedding models are widely used in natural language processing applications. However, their capability is often benchmarked on tasks that do not require understanding nuanced numerical information in text. As a result, it remains unclear whether current embedding models can precisely encode numerical content, such as numbers, into embeddings. This question is critical because embedding models are increasingly applied in domains where numbers matter, such as finance and healthcare. For example, Company X's market share grew by 2\% should be interpreted very differently from Company X's market share grew by 20\%, even though both indicate growth in market share. This study aims to examine whether text embedding models can capture such nuances. Using synthetic data in a financial context, we evaluate 13 widely used text embedding models and find that they generally struggle to capture numerical details accurately. Our further analyses provide deeper insights into embedding numeracy, informing future research to strengthen embedding model-based NLP systems with improved capacity for handling numerical content.
Morphological Perceptron with Competitive Layer: Training Using Convex-Concave Procedure
Venue: arXiv cs.LG 2025
Year: 2025

A morphological perceptron is a multilayer feedforward neural network in which neurons perform elementary operations from mathematical morphology. For multiclass classification tasks, a morphological perceptron with a competitive layer (MPCL) is obtained by integrating a winner-take-all output layer into the standard morphological architecture. The non-differentiability of morphological operators renders gradient-based optimization methods unsuitable for training such networks. Consequently, alternative strategies that do not depend on gradient information are commonly adopted. This paper proposes the use of the convex-concave procedure (CCP) for training MPCL networks. The training problem is formulated as a difference of convex (DC) functions and solved iteratively using CCP, resulting in a sequence of linear programming subproblems. Computational experiments demonstrate the effectiveness of the proposed training method in addressing classification tasks with MPCL networks.
Offline vs. Online Learning in Model-based RL: Lessons for Data Collection Strategies
Venue: arXiv cs.LG 2025
Year: 2025

Data collection is crucial for learning robust world models in model-based reinforcement learning. The most prevalent strategies are to actively collect trajectories by interacting with the environment during online training or training on offline datasets. At first glance, the nature of learning task-agnostic environment dynamics makes world models a good candidate for effective offline training. However, the effects of online vs. offline data on world models and thus on the resulting task performance have not been thoroughly studied in the literature. In this work, we investigate both paradigms in model-based settings, conducting experiments on 31 different environments. First, we showcase that online agents outperform their offline counterparts. We identify a key challenge behind performance degradation of offline agents: encountering Out-Of-Distribution states at test time. This issue arises because, without the self-correction mechanism in online agents, offline datasets with limited state space coverage induce a mismatch between the agent's imagination and real rollouts, compromising policy training. We demonstrate that this issue can be mitigated by allowing for additional online interactions in a fixed or adaptive schedule, restoring the performance of online training with limited interaction data. We also showcase that incorporating exploration data helps mitigate the performance degradation of offline agents. Based on our insights, we recommend adding exploration data when collecting large datasets, as current efforts predominantly focus on expert data alone.
Multi-LVI-SAM: A Robust LiDAR-Visual-Inertial Odometry for Multiple Fisheye Cameras
Venue: arXiv cs.CV 2025
Year: 2025

We propose a multi-camera LiDAR-visual-inertial odometry framework, Multi-LVI-SAM, which fuses data from multiple fisheye cameras, LiDAR and inertial sensors for highly accurate and robust state estimation. To enable efficient and consistent integration of visual information from multiple fisheye cameras, we introduce a panoramic visual feature model that unifies multi-camera observations into a single representation. The panoramic model serves as a global geometric optimization framework that consolidates multi-view constraints, enabling seamless loop closure and global pose optimization, while simplifying system design by avoiding redundant handling of individual cameras. To address the triangulation inconsistency caused by the misalignment between each camera's frame and the panoramic model's frame, we propose an extrinsic compensation method. This method improves feature consistency across views and significantly reduces triangulation and optimization errors, leading to more accurate pose estimation. We integrate the panoramic visual feature model into a tightly coupled LiDAR-visual-inertial system based on a factor graph. Extensive experiments on public datasets demonstrate that the panoramic visual feature model enhances the quality and consistency of multi-camera constraints, resulting in higher accuracy and robustness than existing multi-camera LiDAR-visual-inertial systems.
Enhancing Factual Accuracy and Citation Generation in LLMs via Multi-Stage Self-Verification
Venue: arXiv cs.CL 2025
Year: 2025

This research introduces VeriFact-CoT (Verified Factual Chain-of-Thought), a novel method designed to address the pervasive issues of hallucination and the absence of credible citation sources in Large Language Models (LLMs) when generating complex, fact-sensitive content. By incorporating a multi-stage mechanism of 'fact verification-reflection-citation integration,' VeriFact-CoT empowers LLMs to critically self-examine and revise their intermediate reasoning steps and final answers. This process significantly enhances the objective accuracy, trustworthiness, and traceability of the generated outputs, making LLMs more reliable for applications demanding high fidelity such as scientific research, news reporting, and legal consultation.
Toward Efficient and Scalable Design of In-Memory Graph-Based Vector Search
Venue: arXiv cs.IR 2025
Year: 2025

Vector data is prevalent across business and scientific applications, and its popularity is growing with the proliferation of learned embeddings. Vector data collections often reach billions of vectors with thousands of dimensions, thus, increasing the complexity of their analysis. Vector search is the backbone of many critical analytical tasks, and graph-based methods have become the best choice for analytical tasks that do not require guarantees on the quality of the answers. Although several paradigms (seed selection, incremental insertion, neighborhood propagation, neighborhood diversification, and divide-and-conquer) have been employed to design in-memory graph-based vector search algorithms, a systematic comparison of the key algorithmic advances is still missing. We conduct an exhaustive experimental evaluation of twelve state-of-the-art methods on seven real data collections, with sizes up to 1 billion vectors. We share key insights about the strengths and limitations of these methods; e.g., the best approaches are typically based on incremental insertion and neighborhood diversification, and the choice of the base graph can hurt scalability. Finally, we discuss open research directions, such as the importance of devising more sophisticated data adaptive seed selection and diversification strategies.
Posterior shape models revisited: Improving 3D reconstructions from partial data using target specific models
Venue: arXiv cs.CV 2025
Year: 2025

In medical imaging, point distribution models are often used to reconstruct and complete partial shapes using a statistical model of the full shape. A commonly overlooked, but crucial factor in this reconstruction process, is the pose of the training data relative to the partial target shape. A difference in pose alignment of the training and target shape leads to biased solutions, particularly when observing small parts of a shape. In this paper, we demonstrate the importance of pose alignment for partial shape reconstructions and propose an efficient method to adjust an existing model to a specific target. Our method preserves the computational efficiency of linear models while significantly improving reconstruction accuracy and predicted variance. It exactly recovers the intended aligned model for translations, and provides a good approximation for small rotations, all without access to the original training data. Hence, existing shape models in reconstruction pipelines can be adapted by a simple preprocessing step, making our approach widely applicable in plug-and-play scenarios.
Performance of Conformal Prediction in Capturing Aleatoric Uncertainty
Venue: arXiv cs.LG 2025
Year: 2025

Conformal prediction is a model-agnostic approach to generating prediction sets that cover the true class with a high probability. Although its prediction set size is expected to capture aleatoric uncertainty, there is a lack of evidence regarding its effectiveness. The literature presents that prediction set size can upper-bound aleatoric uncertainty or that prediction sets are larger for difficult instances and smaller for easy ones, but a validation of this attribute of conformal predictors is missing. This work investigates how effectively conformal predictors quantify aleatoric uncertainty, specifically the inherent ambiguity in datasets caused by overlapping classes. We perform this by measuring the correlation between prediction set sizes and the number of distinct labels assigned by human annotators per instance. We further assess the similarity between prediction sets and human-provided annotations. We use three conformal prediction approaches to generate prediction sets for eight deep learning models trained on four datasets. The datasets contain annotations from multiple human annotators (ranging from five to fifty participants) per instance, enabling the identification of class overlap. We show that the vast majority of the conformal prediction outputs show a very weak to weak correlation with human annotations, with only a few showing moderate correlation. These findings underscore the necessity of critically reassessing the prediction sets generated using conformal predictors. While they can provide a higher coverage of the true classes, their capability in capturing aleatoric uncertainty and generating sets that align with human annotations remains limited.
Programming tension in 3D printed networks inspired by spiderwebs
Venue: arXiv cs.GR 2025
Year: 2025

Each element in tensioned structural networks -- such as tensegrity, architectural fabrics, or medical braces/meshes -- requires a specific tension level to achieve and maintain the desired shape, stability, and compliance. These structures are challenging to manufacture, 3D print, or assemble because flattening the network during fabrication introduces multiplicative inaccuracies in the network's final tension gradients. This study overcomes this challenge by offering a fabrication algorithm for direct 3D printing of such networks with programmed tension gradients, an approach analogous to the spinning of spiderwebs. The algorithm: (i) defines the desired network and prescribes its tension gradients using the force density method; (ii) converts the network into an unstretched counterpart by numerically optimizing vertex locations toward target element lengths and converting straight elements into arcs to resolve any remaining error; and (iii) decomposes the network into printable toolpaths; Optional additional steps are: (iv) flattening curved 2D networks or 3D networks to ensure 3D printing compatibility; and (v) automatically resolving any unwanted crossings introduced by the flattening process. The proposed method is experimentally validated using 2D unit cells of viscoelastic filaments, where accurate tension gradients are achieved with an average element strain error of less than 1.0\%. The method remains effective for networks with element minimum length and maximum stress of 5.8 mm and 7.3 MPa, respectively. The method is used to demonstrate the fabrication of three complex cases: a flat spiderweb, a curved mesh, and a tensegrity system. The programmable tension gradient algorithm can be utilized to produce compact, integrated cable networks, enabling novel applications such as moment-exerting structures in medical braces and splints.
A Simple and Robust Protocol for Distributed Counting
Venue: arXiv cs.DC 2025
Year: 2025

We revisit the distributed counting problem, where a server must continuously approximate the total number of events occurring across $k$ sites while minimizing communication. The communication complexity of this problem is known to be $Θ(\frac{k}ε\log N)$ for deterministic protocols. Huang, Yi, and Zhang (2012) showed that randomization can reduce this to $Θ(\frac{\sqrt{k}}ε\log N)$, but their analysis is restricted to the {\em oblivious setting}, where the stream of events is independent of the protocol's outputs. Xiong, Zhu, and Huang (2023) presented a robust protocol for distributed counting that removes the oblivious assumption. However, their communication complexity is suboptimal by a $polylog(k)$ factor and their protocol is substantially more complex than the oblivious protocol of Huang et al. (2012). This left open a natural question: could it be that the simple protocol of Huang et al. (2012) is already robust? We resolve this question with two main contributions. First, we show that the protocol of Huang et al. (2012) is itself not robust by constructing an explicit adaptive attack that forces it to lose its accuracy. Second, we present a new, surprisingly simple, robust protocol for distributed counting that achieves the optimal communication complexity of $O(\frac{\sqrt{k}}ε \log N)$. Our protocol is simpler than that of Xiong et al. (2023), perhaps even simpler than that of Huang et al. (2012), and is the first to match the optimal oblivious complexity in the adaptive setting.
MedFactEval and MedAgentBrief: A Framework and Workflow for Generating and Evaluating Factual Clinical Summaries
Venue: arXiv cs.CL 2025
Year: 2025

Evaluating factual accuracy in Large Language Model (LLM)-generated clinical text is a critical barrier to adoption, as expert review is unscalable for the continuous quality assurance these systems require. We address this challenge with two complementary contributions. First, we introduce MedFactEval, a framework for scalable, fact-grounded evaluation where clinicians define high-salience key facts and an "LLM Jury"--a multi-LLM majority vote--assesses their inclusion in generated summaries. Second, we present MedAgentBrief, a model-agnostic, multi-step workflow designed to generate high-quality, factual discharge summaries. To validate our evaluation framework, we established a gold-standard reference using a seven-physician majority vote on clinician-defined key facts from inpatient cases. The MedFactEval LLM Jury achieved almost perfect agreement with this panel (Cohen's kappa=81%), a performance statistically non-inferior to that of a single human expert (kappa=67%, P < 0.001). Our work provides both a robust evaluation framework (MedFactEval) and a high-performing generation workflow (MedAgentBrief), offering a comprehensive approach to advance the responsible deployment of generative AI in clinical workflows.